Preprints
https://doi.org/10.5194/egusphere-2022-578
https://doi.org/10.5194/egusphere-2022-578
12 Jul 2022
 | 12 Jul 2022

A deep learning approach to increase the value of satellite data for PM2.5 monitoring in China

Bo Li, Cheng Liu, Qihou Hu, Mingzhai Sun, Chengxin Zhang, Shulin Zhang, Yizhi Zhu, Ting Liu, Yike Guo, Gregory R. Carmichael, and Meng Gao

Abstract. Limitations in the current capability of monitoring PM2.5 adversely impact air quality management and health risk assessment of PM2.5 exposure. Commonly, ground-based monitoring networks are established to measure the PM2.5 concentrations in highly populated regions and protected areas such as national parks, yet large gaps exist in spatial coverage. Satellite-derived aerosol optical properties serve to complement the missing spatial information of ground-based monitoring networks. However, such attempts are hampered under cloudy/hazy conditions or during nighttime. Here we strive to overcome the long-standing restriction that surface PM2.5 cannot be constrained with satellite remote sensing under cloudy/hazy conditions or during nighttime. We introduce a deep spatiotemporal neural network (ST-NN) and demonstrate that it can artfully fill these observational gaps. We use sensitivity analysis and visualization technology to open the neural network black box data model, and quantitatively discuss the potential impact of the input data on the target variables. This technique provides ground-level PM2.5 concentrations with high spatial resolution (0.01°) and 24-hour temporal coverage. Better constrained spatiotemporal distributions of PM2.5 concentrations will help improve health effects studies, atmospheric emission estimates, and predictions of air quality.

Bo Li, Cheng Liu, Qihou Hu, Mingzhai Sun, Chengxin Zhang, Shulin Zhang, Yizhi Zhu, Ting Liu, Yike Guo, Gregory R. Carmichael, and Meng Gao

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on egusphere-2022-578', Hua Lin, 23 Aug 2022
    • AC1: 'Reply on CC1', Bo Li, 25 Aug 2022
  • RC1: 'Comment on egusphere-2022-578', Anonymous Referee #1, 01 Sep 2022
  • RC2: 'Comment on egusphere-2022-578', Anonymous Referee #2, 30 Sep 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on egusphere-2022-578', Hua Lin, 23 Aug 2022
    • AC1: 'Reply on CC1', Bo Li, 25 Aug 2022
  • RC1: 'Comment on egusphere-2022-578', Anonymous Referee #1, 01 Sep 2022
  • RC2: 'Comment on egusphere-2022-578', Anonymous Referee #2, 30 Sep 2022
Bo Li, Cheng Liu, Qihou Hu, Mingzhai Sun, Chengxin Zhang, Shulin Zhang, Yizhi Zhu, Ting Liu, Yike Guo, Gregory R. Carmichael, and Meng Gao

Data sets

POI the Resource and Environment Science Data Center https://www.resdc.cn/data.aspx?DATAID=330

GDP the Resource and Environment Science Data Center https://www.resdc.cn/data.aspx?DATAID=252

Population the Resource and Environment Science Data Center https://www.resdc.cn/data.aspx?DATAID=251

MODIS land cover type Mark Friedl https://doi.org/10.5067/MODIS/MCD12C1.006

DEM the Resource and Environment Science Data Center https://www.resdc.cn/data.aspx?DATAID=123

MODIS aerosol optical depth Rob Levy and Christina Hsu https://doi.org/10.5067/MODIS/MOD04_3K.061

Himawari-8 satellite aerosol optical depth Yoshida, M. https://doi.org/10.2151/jmsj.2018-039

site pm2.5 CNEMC http://www.cnemc.cn/

weather fields the National Centers for Environment Prediction https://www.mmm.ucar.edu/weather-research-and-forecasting-model

road network openstreetmap https://download.geofabrik.de/asia/china.html

Bo Li, Cheng Liu, Qihou Hu, Mingzhai Sun, Chengxin Zhang, Shulin Zhang, Yizhi Zhu, Ting Liu, Yike Guo, Gregory R. Carmichael, and Meng Gao

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Latest update: 24 Feb 2024
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Short summary
Ambient particles have an important impact on human health, meteorology and climate change. By building a deep spatiotemporal neural network model we have overcome the long-standing limitations and get the full time and space coverage ground PM2.5 concentrations. We open the neural network black box data model by using sensitivity analysis and visualization techniques. This research will help improve health effects studies, climate effects of aerosols, and air quality prediction.